In recent years, networks and interconnectivity of individuals, groups, and organizations has dramatically increased. The Internet connects the world by joining billions of connected users that represent various entities, information, and resources. These connected users form enormous banks of resources, resulting in a world wide web of users. The users store and access documents or web pages, identified by uniform resource locators (URL), that can be accessed by other connected nodes on the network. This vast data store allows previously obscure or unknown information to be disseminated throughout the world. The users perform a wide range of activities such as accessing information sources including news, weather, sports, and financial sites. Other users buy and sell products and services in electronic commerce systems.
One of the primary applications of the Web has been shopping, that is, the purchase of goods and services. Virtually every major commercial “brick and mortar” merchant has established a Web site for the showcase and sale of their products. Further, many manufacturers sell products directly over the Web. Finally, a plethora of on-line merchants, not previously existing in the brick and mortar world, have come into existence. As a result, virtually every product is available for purchase over the Web from a plurality of merchants. This situation has increased the efficiency of markets by permitting shoppers to readily compare products and terms of sale from plural merchants without the need to physically travel to the merchant locations.
With this increase in efficiency of markets has come an increased burden on the consumer of these products. To determine the best quality, lowest price product now requires a consumer to sift through volumes and volumes of potential providers. To reduce the number of irrelevant product providers and to increase the quality of a consumer's search, information regarding potential providers may be filtered to deliver the most relevant providers to the user.
Information filtering is performed in a number of ways. For example, a customary consumer telephone directory of businesses, such as the Yellow Pages, filters product providers by geographic calling area. Further, Internet Service Providers and Internet portals also classify information by categorizing web pages by topics such as news, sports, entertainment, and the like. However, these broad subject areas are not always sufficient to locate information of interest to a consumer.
More sophisticated techniques for filtering products of interest to consumers may be employed by identifying information about the user. These methods may monitor and record a consumer's purchase behavior or other patterns of behavior. Information may be collected by means of surveys, questionnaires, opinion polls, and the like. These conventional techniques may be extrapolated to the networked world by means of inferential tracking programs, cookies, and other techniques designed to obtain consumer information with minimal consumer effort and minimal expenditure of resources.
Information may be transferred and stored on a consumer's computer by a web server to monitor and record information related to a user's web-related activities. The user's web-related information may include information about product browsing, product selections, and purchases made by the user at web pages hosted by a web server. The information stored by the inferential tracking programs is typically accessed and used by the web server when the particular server or web page is again accessed by the user computer. Cookies may be used by web servers to identify users, to instruct the server to send a customized version of the requested web page to the client computer, to submit account information for the user, and so forth. Explicit and implicit user information collection techniques are used by a large number of web-based providers of goods and services including eBay®, Amazon™, and others. In some instances, user information gathered by the servers is used to create personalized profiles for the users. The customized profiles are then used to summarize the user's activities at one or more web pages associated with the server.
Current shopping advisory systems focus on enhanced shopping carts to provide suggested additional products a user may purchase, while others have developed advisory systems to provide product recommendations based in part on a vendor payment to sort the vendor's product to the top of the list.
Conventional shopping advisory systems focus on a point of sale event and only take into account a user's imminent product purchase and possibly prior purchases from the specific merchant. These prior systems do not cover all related products a user acquired from a variety of sources.
Further, these conventional systems do not utilize user profile information based on collected demographics, user ratings, and behavioral data. Without this profile data, conventional systems do not provide personalized product information.
Finally, conventional systems typically do not incorporate unbiased professional editorial product reviews and ratings or end-user product reviews and ratings. Because they lack this editorial data, the typical advisory systems do not factor editorial rankings into the purchase advice.
Filtering methods based upon the content of the user's activities may be used to reach information, goods, and services for the user based upon correlations between the user's activities and the items. The filtering methods and customized profiles may then be used to recommend or suggest additional information, goods, and services in which the user may be interested.
Filtering methods serve to organize the array of information, goods, and services to assist the user by presenting materials that the user is more likely to be interested in, or by directing the user to materials that the user may find useful. Filtering attempts to sift through the vast stores of information while detecting and uncovering less conspicuous information that may be of interest to the user. The filtering methods attempt to locate items of meaningful information that would otherwise be obscured by the volume of irrelevant information vying for the attention of the user.
Information filtering may be directed to content-based filtering where keywords or key articles are examined and semantic and syntactic information are used to determine a user's interests. Additionally, expert systems may be utilized to “learn” a user's behavior patterns. For example, expert systems or intelligent software agents may note a user's actions in response to a variety of stimuli and then respond in the same manner when similar stimuli present in the future.
As expert systems grow, or as intelligent software agents expand to cover additional users or groups, the range and accuracy of the responses may be refined to increase the efficiency of the system. Collaboration among users or groups of like users results in increased accuracy with regard to predicting future user responses based upon past responses. Evaluating feedback of other similar users is effective in determining how a similar user will respond to similar stimuli. Users that agreed in the past will likely agree in the future. These collaborative filtering methods may use weighted averaging techniques for user feedback that extracts ratings for articles such as information, goods, services, and the like, to predict whether an article is relevant to a particular user. With weighted averages, however, the character of the content is ignored or otherwise obscured during the averaging process because personal preferences, credibility, and other factors are lost.
What is needed is a system and a method of combining user profile information with collaborative and editorial data to provide users with credible information regarding information, goods, and services.